Image Decoding with Time-Distance-Aware BDOF Prediction
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Solution Overview
Problem
Conventional BDOF processing assumes equal time distances between reference frames without considering the actual temporal distances, leading to inefficient prediction signal generation.
Innovation Solution
An image decoding device and method that sets application conditions for BDOF processing based on weight coefficients, considering the time distances between reference frames and the target frame, and incorporates PROF processing for gradient calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If BDOF processing uses equal weights for pixel values from two reference frames, then the calculation is simplified, but the prediction accuracy deteriorates because it ignores actual time distances between frames
Solution Approach 1:
The patent changes the parameter of weight coefficients from equal weights to time-distance-based weights. Specifically, it introduces a weight coefficient calculation that considers the actual time distances between the target frame and reference frames, allowing the prediction accuracy to improve while maintaining manageable calculation complexity through standardized formulas.
Solution Approach 2:
The patent makes the weight coefficients dynamic by adjusting them according to the actual time distances between frames. Instead of using fixed equal weights, the system dynamically calculates weights based on temporal relationships, enabling the prediction to adapt to varying time distances between reference and target frames.
2Productivity
If BDOF processing assumes equal time distances between reference frames, then the processing is simplified, but the prediction accuracy deteriorates due to incorrect weight coefficients
Solution Approach 1:
The patent changes the parameter assumption from equal time distances to actual time distances. It introduces a mechanism to calculate and use actual time distances between the target frame and reference frames, thereby improving prediction accuracy while maintaining processing efficiency through systematic parameter adjustment.
3Device complexity
If the application condition of BDOF does not consider time distances, then the processing is simpler, but the prediction quality deteriorates
Solution Approach 1:
The patent modifies the application condition parameters to include time distance considerations. It introduces a weight coefficient calculation that incorporates actual time distances, ensuring that BDOF processing is applied with appropriate weights that reflect temporal relationships, thereby improving prediction quality without excessive complexity.
4Adaptability or versatility
If PROF processing uses separate gradient calculations, then the processing is more flexible, but the device complexity increases
Solution Approach 1:
The patent merges the gradient calculation processing between BDOF and PROF. It introduces a unified gradient calculation mechanism that serves both processing modes, reducing device complexity while maintaining the flexibility and adaptability benefits of separate processing through a shared computational foundation.
Data Source
AI summary
An image decoding device includes: a prediction signal generation unit configured to determine whether or not an application condition of Bi-Directional Optical Flow (BDOF) processing is satisfied for each block, generate a prediction signal by executing the BDOF processing in a case where it is determined that the application condition is satisfied, and set the application condition based on a weight coefficient in a case where calculation is performed using pixel values of two reference frames or values calculated from the pixel values in the BDOF processing such that the application condition includes a condition on time distances between the two reference frames and a target frame.